Review - A Graph Convolutional Neural Network for Emotion Recognition in Conversation

The paper "DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation" presents an innovative solution for emotion recognition in conversations with multiple speakers. It leverages the Relational Graph Convolutional Network to model dependencies between utterances, overcoming limitations of existing RNNs-based approaches that ignore speaker-level dependency. The proposed DialogueGCN model uses bidirectional GRUs to embed sequential based utterances and construct a relational graph with learnable weights for edge relations. This approach effectively captures both temporal and speaker dependencies, demonstrating superiority in recognizing emotions in multi-party conversations with long time-frame inputs.